Design of a Support System for Complicated Logistics Location Integrating Big Data

Abstract

Logistics location is an important component of logistics planning that affects traffic pressure and vehicle emissions. To date, there has not been an adequate study of the integration of big data into the location for a complicated logistics system. This study developed a decision support system that can address location problems for complicated logistics systems, e.g., a multilevel urban underground logistics system (ULS), using logistics big data. First, information needed in the logistics location, such as the traffic performance index (TPI) and the origin/destination (OD) matrix, was collected and calculated using a big data platform, and this information was digitized and represented based on a geographic information system (GIS) tool. Second, a two-stage location model for a ULS was designed to balance the construction costs and traffic congestion. The first stage is establishing a set-covering model to identify optimum locations for secondary hubs based on the ant colony optimization algorithm, and the second stage is clustering of the secondary hubs to determine locations for primary hubs using the iterative self-organizing data analysis technique algorithm (ISODATA). Finally, the Xianlin district of Nanjing, China, was chosen as a case study to validate the effectiveness of the proposed system. The system can be used to facilitate logistics network planning and to promote the application of big data in logistics.

Research on the network connection mode of logistics economy in Guangdong province based on social network analysis

“…, 2014; Rivera et al. , 2014, 2016; Kayikci, 2010; Sheffi, 2012), logistics warehouse location and path optimization (Cheng et al. , 2021; Liu and Ying, 2020; Shahparvari et al.…”

“…With the development of data economy, the research of logistics industry has gradually shifted from the macro perspective to the micro perspective, Logistics enterprise location and route optimization have become the focus of international scholars, Scholars optimize the location of complex logistics system through platform data and geographic information system (Cheng et al. , 2021), Liu Peide et al.…”

Modelo Matemático De Integración De Producción E Inventarios en Una Red Logística Plantas, Distribuidores Y Detallistas

“…Los componentes de costo incluyen el costo de producción, almacenamiento, alistamiento de las fuentes premisas (plantas de producción) y el costo de transporte, costos de ordenar e inventario de los puntos de destino [16]. Obtener la integración de las decisiones en la red logística para minimizar los costos de operatividad es un fin justificable [17].…”

Illustrating scholar–practitioner collaboration for data-driven decision-making in the optimization of logistics facility location and implications for increasing the adoption of AR and VR practices

“…The pertinence of scientific research for addressing the challenges of Logistics 4.0 Mining the hidden business values from large-scale logistic data produced by IoT and cloud computing technologies promote the development of smart logistics (Song et al, 2021;Cheng and Pan, 2021). As generated data continue to grow in size and complexity (Kaffash et al, 2021), it becomes necessary to manage and control this massive volume of data through datadriven models.…”

“…It is through scientific research that practitioners can stay abreast of the challenges, complexities and the changing contexts under which the logistics industry is evolving. The existing literature suggests that logistics-related big data models are context-specific and must be adapted to different scenarios and logistics processes, while few studies have developed an integrated platform that includes logistics data search and collection, storage, visualization and optimization (Cheng and Pan, 2021). Doing so involves specific capabilities and competencies for efficiently and accurately handling and analyzing the quantity and size of data involved in this process which many enterprises, like LPRa resourceful and resource-rich multinational enterprisedo not possess in-house.…”